PTaylor18/Machine-Learning-Summer-Code

Repository containing code for my learning of classical and quantum machine learning. Includes a variational quantum classifier, fully-connected and quantum convolutional neural networks as well as benchmarks.

24
/ 100
Experimental

This repository provides working code examples and benchmarks for various machine learning models, including classical convolutional neural networks and several quantum machine learning architectures like Variational Quantum Classifiers and Quantum Convolutional Neural Networks. It takes in datasets (such as image data like MNIST or custom feature sets) and outputs classification results and performance comparisons for different models. This is ideal for researchers or students exploring and comparing different classical and quantum machine learning approaches for classification tasks.

No commits in the last 6 months.

Use this if you are a quantum machine learning researcher or student who wants to explore and compare different quantum neural network architectures and their performance on classification problems.

Not ideal if you are looking for a production-ready quantum machine learning library or a tool for general-purpose classical machine learning development.

quantum-machine-learning quantum-computing classification algorithm-benchmarking quantum-neural-networks
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 11 / 25

How are scores calculated?

Stars

13

Forks

2

Language

Jupyter Notebook

License

Last pushed

Nov 02, 2021

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/PTaylor18/Machine-Learning-Summer-Code"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.